





from __future__ import annotations

import math




def      __UpperCamelCase    (_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						)							->  Optional[int]:
  lowercase__           =    u
  for i in range(1						,		_SCREAMING_SNAKE_CASE						):
    lowercase__           =    temp * (u - i)
  return temp




def      __UpperCamelCase    ()							->  Dict:
  lowercase__           =    int(input('enter the numbers of values: '						)						)
  lowercase__           =    []
  for _ in range(_SCREAMING_SNAKE_CASE						):
    y.append([]						)
  for i in range(_SCREAMING_SNAKE_CASE						):
    for j in range(_SCREAMING_SNAKE_CASE						):
      y[i].append(_SCREAMING_SNAKE_CASE						)
      lowercase__           =    0

  print('enter the values of parameters in a list: '						)
  lowercase__           =    list(map(_SCREAMING_SNAKE_CASE						,		input().split()						)						)

  print('enter the values of corresponding parameters: '						)
  for i in range(_SCREAMING_SNAKE_CASE						):
    lowercase__           =    float(input()						)

  lowercase__           =    int(input('enter the value to interpolate: '						)						)
  lowercase__           =    (value - x[0]) / (x[1] - x[0])

  # for calculating forward difference table

  for i in range(1						,		_SCREAMING_SNAKE_CASE						):
    for j in range(n - i						):
      lowercase__           =    y[j + 1][i - 1] - y[j][i - 1]

  lowercase__           =    y[0][0]
  for i in range(1						,		_SCREAMING_SNAKE_CASE						):
    summ += (ucal(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						) * y[0][i]) / math.factorial(_SCREAMING_SNAKE_CASE						)

  print(F"""the value at {value} is {summ}"""						)


if __name__ == "__main__":
      main()



import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()





def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				torch.load(lowercase				,     map_location='''cpu'''				)

				__lowercase					     =				chkpt['''model''']

				# We have the base model one level deeper than the original XLM repository
				__lowercase					     =				{}
				for k, v in state_dict.items():
								if "pred_layer" in k:
												__lowercase					     =				v
								else:
												__lowercase					     =				v

				__lowercase					     =				chkpt['''params''']
				__lowercase					     =				{n: v for n, v in config.items() if not isinstance(lowercase				,     (torch.FloatTensor, numpy.ndarray)				)}

				__lowercase					     =				chkpt['''dico_word2id''']
				__lowercase					     =				{s + '''</w>''' if s.find('''@@'''				) == -1 and i > 13 else s.replace('''@@'''				,     ''''''				): i for s, i in vocab.items()}

				# Save pytorch-model
				__lowercase					     =				pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
				__lowercase					     =				pytorch_dump_folder_path + '''/''' + CONFIG_NAME
				__lowercase					     =				pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

				print(F"Save PyTorch model to {pytorch_weights_dump_path}"				)
				torch.save(lowercase				,     lowercase				)

				print(F"Save configuration file to {pytorch_config_dump_path}"				)
				with open(lowercase				,     '''w'''				,     encoding='''utf-8'''				) as f:
								f.write(json.dumps(lowercase				,     indent=2				) + '''\n'''				)

				print(F"Save vocab file to {pytorch_config_dump_path}"				)
				with open(lowercase				,     '''w'''				,     encoding='''utf-8'''				) as f:
								f.write(json.dumps(lowercase				,     indent=2				) + '''\n'''				)


if __name__ == "__main__":
		__a    :	Optional[int]         =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--xlm_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
		)
		__a    :	Any         =    parser.parse_args()
		convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)